\begin{table}[t]
\centering
\caption{\textbf{Effect of Voter ID Law on 2016 Primary Election Turnout, by Pre-Treatment Turnout.}
\label{tab:primary_heterogeneity}}
\begin{tabular}{l|ccccc}
\toprule \toprule
 & \multicolumn{5}{c}{\# of Pre-Treatment General Elections}\\
 & 0 & 1 & 2 & 3 & 4 \\
\midrule
 \# of Pre-Treatment Primary Elections & & & & \medskip \\
0  & -0.004 & -0.008 & -0.006 & -0.020 & -0.018 \\
 & (0.000) & (0.000) & (0.000) & (0.001) & (0.001) \smallskip\\
1  & -0.010 & -0.023 & -0.023 & -0.029 & -0.019 \\
 & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) \smallskip\\
2  & -0.010 & -0.017 & -0.037 & -0.031 & -0.021 \\
 & (0.014) & (0.006) & (0.003) & (0.002) & (0.001) \smallskip\\
3  & -0.018 & -0.008 & -0.027 & -0.036 & -0.015 \\
 & (0.065) & (0.017) & (0.006) & (0.003) & (0.000) \smallskip\\
4  &  & 0.005 & -0.012 & -0.017 & -0.011 \\
 & & (0.005) & (0.004) & (0.002) & (0.000) \smallskip\\
\bottomrule \bottomrule
\multicolumn{6}{p{0.9\textwidth}}{\footnotesize Each cell estimates the effect of the voter ID law on 
2016 primary turnout, estimating the effect separately for different pre-treatment turnout patterns.  
We construct strata of treated and control units based on the total number of times a voter casted a ballot 
in a pre-treatment primary election (2008-2014) and pre-treatment general election (2008-2014).  
We implement the same exact matching procedure described in Section \ref{sec:primary_effect}.  
Robust standard errors are in parentheses.}
\end{tabular}
\end{table}
